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Researchers at the Hebrew University of Jerusalem recently discovered that in Retrieval Augmented Generation (RAG) systems, the number of documents processed significantly impacts language model performance, even when the total text length remains constant. The research team conducted experiments using 2,417 questions from the MuSiQue validation dataset, each linked to 20 Wikipedia paragraphs. Two to four paragraphs contained relevant answer information, with the remaining paragraphs serving as distractors. To study the impact of the number of documents, the team created multiple data partitions, progressively reducing the number of documents from 20 to...

On March 27th, Alibaba launched its first multimodal large model, Qwen2.5-Omni-7B. This model boasts powerful capabilities, handling various input modalities such as text, images, audio, and video, and generating text and natural speech outputs in real-time. This innovative technological breakthrough marks another significant advancement for Alibaba in the field of artificial intelligence. In the authoritative OmniBench multimodal fusion task benchmark, Qwen2.5-Omni achieved...

The Alibaba Cloud Tongyi Qianwen Qwen team announced the launch of Qwen2.5-Omni, a new generation of end-to-end multimodal flagship model in the Qwen family. Designed for comprehensive multimodal understanding, this new model seamlessly handles various input formats including text, images, audio, and video, and generates text and natural speech synthesis outputs simultaneously via real-time streaming response.

Alibaba is making waves in the AI field with the recent open-source release of its latest multimodal model, Qwen2.5-VL-32B-Instruct. This model is part of the Qwen2.5 series, which also includes 3B, 7B, and 72B versions. The 32B version prioritizes convenient local execution while maintaining performance. Enhanced through reinforcement learning, Qwen2.5-VL-32B excels in several areas. Notably, its responses are more aligned with human expectations.

A powerful newcomer has emerged in the fintech arena. The Fin-R1 model, jointly developed by Professor Liwen Zhang's team (SUFE-AIFLM-Lab) at the School of Statistics and Data Science, Shanghai University of Finance and Economics, and Caiyue Xingchen, has been officially open-sourced, attracting significant attention due to its impressive performance. This financial specialized large language model, based on Qwen2.5-7B and trained with reinforcement learning, achieves leading performance across multiple financial benchmark tests. Remarkably, Fin-R1 surpasses most models of comparable size, and even many significantly larger models, despite having only 7B parameters.

Manus, an AI Agent product from Chinese startup Monica, has recently gained traction on X. Its founder, Ji Yichao, today shared further technical details via social media. According to Ji, Manus is built upon Alibaba's Qwen large language model and leverages various fine-tuned models to achieve its unique functionalities. This revelation has sparked industry discussion regarding Manus's technological origins and further ignited global AI community interest in its potential.

Reports indicate that the National Supercomputing Internet Platform has integrated Alibaba's Qwen large language model, officially providing the QwQ-32B API service. Users can access up to 1 million tokens free of charge, offering a valuable opportunity for developers and researchers. QwQ-32B is the latest open-source inference model from Alibaba's Tongyi team, demonstrating excellent performance. According to multiple authoritative benchmark tests, QwQ-32B's capabilities rival those of a fully-fledged 671B model.

Tongyi Lingma has announced the launch of its latest inference model, Qwen2.5-Max, offering developers powerful programming and mathematical capabilities. Trained on over 20 trillion tokens and incorporating a meticulously designed post-training scheme, Qwen2.5-Max demonstrates exceptional performance.

On February 25th, Alibaba announced the launch of QwQ-Max-Preview, a reasoning model based on Qwen2.5-Max, and plans to fully open-source its latest reasoning models, QwQ-Max and Qwen2.5-Max. The released QwQ-Max-Preview is a preview version. Alibaba stated that the official version will be released soon and fully open-sourced under the Apache2.0 license. Unlike previous releases, this open-sourcing includes not only the model itself but also smaller versions such as Q...

The National Supercomputing Internet Platform recently announced the launch of the "AI Ecosystem Partner Acceleration Program," offering a series of benefits for enterprise users, including three months of free access to the DeepSeek API and supporting it with a computing power resource pool of ten million core hours. It is reported that the platform has completed the deployment of the full version of the DeepSeek model image. Currently, the platform has achieved interconnection with over 20 supercomputing and intelligent computing centers across 14 provinces, aggregating over 6,500 types of computing products, including nearly 2 AI model services.

IBM recently announced that its AI development platform watsonx.ai now supports the DeepSeek-R1 distilled versions of the Llama 3.18B and Llama 3.370B models. DeepSeek optimizes multiple Llama and Qwen variants using knowledge distillation technology, leveraging data from the R1 model to further enhance model performance. On the watsonx.ai platform, users can utilize DeepSeek in two ways.

Recently, Huggingface, the world's largest open source AI community, released the latest Open LLM Leaderboard. The results show that all ten of the top-ranked open source large models are derivative models that have been fine-tuned based on Alibaba Tongyi Qwen's (Qwen) open-source model. This achievement marks Qwen's dominant position in the open-source AI field and further enhances its global influence. The Open LLM Leaderboard is widely...